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feat(honcho): add configurable observation mode (unified/directional)

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Adds observationMode config field to HonchoClientConfig:
- 'unified' (default): user peer self-observations, all agents share one pool
- 'directional': AI peer observes user, each agent keeps its own view

Changes:
- client.py: observation_mode field, _normalize_observation_mode(), config resolution
- session.py: add_peers respects mode (peer observation flags), dialectic_query
  routes through correct peer, create_conclusion uses correct observer
This commit is contained in:
Erosika
2026-04-02 18:01:48 -04:00
committed by Teknium
parent 9e0fc62650
commit 29c98e8f83
2 changed files with 59 additions and 10 deletions
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19
plugins/memory/honcho/client.py
View File

@@ -85,6 +85,16 @@ def _normalize_recall_mode(val: str) -> str:
return val if val in _VALID_RECALL_MODES else "hybrid"
_VALID_OBSERVATION_MODES = {"unified", "directional"}
_OBSERVATION_MODE_ALIASES = {"shared": "unified", "separate": "directional", "cross": "directional"}
def _normalize_observation_mode(val: str) -> str:
"""Normalize observation mode values."""
val = _OBSERVATION_MODE_ALIASES.get(val, val)
return val if val in _VALID_OBSERVATION_MODES else "unified"
def _resolve_memory_mode(
global_val: str | dict,
host_val: str | dict | None,
@@ -154,6 +164,10 @@ class HonchoClientConfig:
# "context" — auto-injected context only, Honcho tools removed
# "tools" — Honcho tools only, no auto-injected context
recall_mode: str = "hybrid"
# Observation mode: how Honcho peers observe each other.
# "unified" — user peer observes self; all agents share one observation pool
# "directional" — AI peer observes user; each agent keeps its own view
observation_mode: str = "unified"
# Session resolution
session_strategy: str = "per-directory"
session_peer_prefix: bool = False
@@ -313,6 +327,11 @@ class HonchoClientConfig:
or raw.get("recallMode")
or "hybrid"
),
observation_mode=_normalize_observation_mode(
host_block.get("observationMode")
or raw.get("observationMode")
or "unified"
),
session_strategy=session_strategy,
session_peer_prefix=session_peer_prefix,
sessions=raw.get("sessions", {}),

50
plugins/memory/honcho/session.py
View File

@@ -110,6 +110,9 @@ class HonchoSessionManager:
self._dialectic_max_chars: int = (
config.dialectic_max_chars if config else 600
)
self._observation_mode: str = (
config.observation_mode if config else "unified"
)
# Async write queue — started lazily on first enqueue
self._async_queue: queue.Queue | None = None
@@ -159,13 +162,18 @@ class HonchoSessionManager:
session = self.honcho.session(session_id)
# Configure peer observation settings.
# observe_me=True for AI peer so Honcho watches what the agent says
# and builds its representation over time — enabling identity formation.
# Configure peer observation settings based on observation_mode.
# Unified: user peer observes self, AI peer passive — all agents share
# one observation pool via user self-observations.
# Directional: AI peer observes user — each agent keeps its own view.
try:
from honcho.session import SessionPeerConfig
user_config = SessionPeerConfig(observe_me=True, observe_others=True)
ai_config = SessionPeerConfig(observe_me=True, observe_others=True)
if self._observation_mode == "directional":
user_config = SessionPeerConfig(observe_me=True, observe_others=False)
ai_config = SessionPeerConfig(observe_me=False, observe_others=True)
else: # unified (default)
user_config = SessionPeerConfig(observe_me=True, observe_others=False)
ai_config = SessionPeerConfig(observe_me=False, observe_others=False)
session.add_peers([(user_peer, user_config), (assistant_peer, ai_config)])
except Exception as e:
@@ -493,12 +501,27 @@ class HonchoSessionManager:
if not session:
return ""
peer_id = session.assistant_peer_id if peer == "ai" else session.user_peer_id
target_peer = self._get_or_create_peer(peer_id)
level = reasoning_level or self._dynamic_reasoning_level(query)
try:
result = target_peer.chat(query, reasoning_level=level) or ""
if self._observation_mode == "directional":
# AI peer queries about the user (cross-observation)
if peer == "ai":
ai_peer_obj = self._get_or_create_peer(session.assistant_peer_id)
result = ai_peer_obj.chat(query, reasoning_level=level) or ""
else:
ai_peer_obj = self._get_or_create_peer(session.assistant_peer_id)
result = ai_peer_obj.chat(
query,
target=session.user_peer_id,
reasoning_level=level,
) or ""
else:
# Unified: user peer queries self, or AI peer queries self
peer_id = session.assistant_peer_id if peer == "ai" else session.user_peer_id
target_peer = self._get_or_create_peer(peer_id)
result = target_peer.chat(query, reasoning_level=level) or ""
# Apply Hermes-side char cap before caching
if result and self._dialectic_max_chars and len(result) > self._dialectic_max_chars:
result = result[:self._dialectic_max_chars].rsplit(" ", 1)[0] + " …"
@@ -895,9 +918,16 @@ class HonchoSessionManager:
logger.warning("No session cached for '%s', skipping conclusion", session_key)
return False
assistant_peer = self._get_or_create_peer(session.assistant_peer_id)
try:
conclusions_scope = assistant_peer.conclusions_of(session.user_peer_id)
if self._observation_mode == "directional":
# AI peer creates conclusion about user (cross-observation)
assistant_peer = self._get_or_create_peer(session.assistant_peer_id)
conclusions_scope = assistant_peer.conclusions_of(session.user_peer_id)
else:
# Unified: user peer creates self-conclusion
user_peer = self._get_or_create_peer(session.user_peer_id)
conclusions_scope = user_peer.conclusions_of(session.user_peer_id)
conclusions_scope.create([{
"content": content.strip(),
"session_id": session.honcho_session_id,